Unlike frequency and decision margin [1]} which are characteristics of the dataset, FNR is a metric of a model's performance. As we continue to update the model's parameters, FNR changes. Therefore, the weights for each class change dynamically to reflect a model's instantaneous performance. We rewrite Eq. REF  using recall (\(1-FNR\) ) with a subscript \(t\)  to denote the time dependency.
\(RecallCE = -\sum _{c=1}^C (1-\frac{TP_{c,t}}{FN_{c,t}+TP_{c,t}}) N_c \log (p^{c,t}) = -\sum _{c=1}^C \sum _{n:y_i=c}(1-\mathcal {R}_{c,t})\log (p_{n,t})\) 
